Visual tracking method based on discriminant dictionary learning

Abstract

The invention belongs to the field of computer vision. The invention particularly relates to complex backgrounds, shielding and other problems. The invention discloses a target tracking method based on discriminant dictionary learning. Firstly, a target and a background sample are obtained according to the local correlation of the target in time and space; secondly, a dictionary learning model isestablished based on sparse representation, an error item is used for capturing abnormal values generated by shielding and the like, a non-convex MCP function is used for punishing a sparse coding matrix and an error matrix, and inconsistent constraint items are applied to the dictionary so as to improve dictionary robustness and discrimination; mM-IALM optimization method is used for solving theproposed non-convex dictionary learning model so as to obtain better convergence; And calculating a candidate target reconstruction error from the obtained dictionary to construct a target observationmodel, and realizing accurate tracking of the target based on a Bayesian reasoning framework. Simulation results show that compared with an existing mainstream algorithm, the method has higher tracking precision and robustness under the environments of illumination change, scale change, shielding, background clutter and the like.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a method for learning visual tracking based on a discriminant dictionary under problems such as complex backgrounds and occlusions. Background technique [0002] Object tracking is one of the challenging research directions in the field of computer vision, and it has a wide range of applications in video surveillance, automatic driving, human-computer interaction, etc. In recent years, visual tracking methods have made remarkable progress, and many efficient and robust tracking algorithms have been proposed. However, many challenging problems remain unsolved, such as illumination changes, scale changes, occlusions, and background clutter, which lead to significant performance degradation of tracking algorithms. Therefore, how to improve the performance of tracking algorithms is a research hotspot in the field of visual tracking. [0003] In response to the above probl...

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Problems solved by technology

However, this non-convex constraint method has not been effectively applied to the field of visual tracking

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